Agglomerative independent variable group analysis

Authors:
Antti Honkela;Jeremias Seppä;Esa Alhoniemi
Affiliations:
Adaptive Informatics Research Centre, Helsinki University of Technology, P.O. Box 5400, FI-02015 TKK, Finland;Adaptive Informatics Research Centre, Helsinki University of Technology, P.O. Box 5400, FI-02015 TKK, Finland;Department of Information Technology, University of Turku, FI-20014, Turku, Finland
Venue:
Neurocomputing
Year:
2008

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Abstract

Independent variable group analysis (IVGA) is a method for grouping dependent variables together while keeping mutually independent or weakly dependent variables in separate groups. In this paper two variants of an agglomerative method for learning a hierarchy of IVGA groupings are presented. The method resembles hierarchical clustering, but the choice of clusters to merge is based on variational Bayesian model comparison. This is approximately equivalent to using a distance measure based on a model-based approximation of mutual information between groups of variables. The approach also allows determining optimal cutoff points for the hierarchy. The method is demonstrated to find sensible groupings of variables that can be used for feature selection and ease construction of a predictive model.